Batch processes have been widely applied in the production and preparation of a variety of products, such as food, polymers and medicines, and play an important role in chemical production and process industries. With the continuous development of computer technology, process control and optimization technology, the quality control and optimization of batch processes have become one of the hot spots studied by the contemporary industrial circle and academic circle, and have great significance for the development of the batch process industries.
At present, a mechanism model-based optimization method is adopted for the quality control and optimization issues of most of the batch processes. It has high optimization efficiency, and can be used for on-line control and optimization, but it must depend on a complex process model to proceed. The flexibility of the batch processes determines that product processing may change at any time, and the batch processes lack conditions for a lot of experiments and a great deal of time required by model identification. Moreover, the nonlinearity, uncertainty and interference of the batch processes are severer than those of continuous processes, bringing a lot of problems to the establishment of accurate and reliable mechanism models for the batch processes.
A data drive-based optimization method does not need priori knowledge about a process, and has obvious advantages. Nevertheless, when operation optimization is carried out for a batch production process, if optimization is carried out only once, the optimization performance is limited. Therefore, how to utilize the repetitive characteristic of a batch process to continuously improve quality indexes and increase production efficiency according to information of historical batches by updating the trajectory of an optimization variable of the current batch by means of an iterative algorithm has become a difficulty to be solved and a focus in the field of batch process optimization.